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Strong brand, mid-level ML/generalist data scientist role, and in-demand skillset increase competition.
Core ML, Python and NLP skills are transferable, though insurance domain experience moderately matters.
Explicit 3-5 years and mandatory ML/NLP/LLM stack make screening highly strict.
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Design, build, validate, and deploy production-grade rule-based and machine learning models using structured and unstructured data.
Perform exploratory data analysis, feature engineering, and monitor/improve model performance.
Ensure model development adheres to quality, security, compliance, explainability, and responsible AI guidelines while collaborating with business, technology, operations, and analytics teams.
Bachelor’s degree in computer science, information technology or equivalent educational qualification.
3-5+ years of relevant experience in data science or related fields.
Proficiency in Python, statistics, hypothesis testing, feature engineering, and machine learning frameworks such as Scikit-learn, Tensorflow, PyTorch.
Experience in NLP technologies (spaCy, Transformers, OCR) and generative AI including large language models and prompt engineering.
Experienced individual contributor capable of building and deploying machine learning models in production environments.
Comfortable working across multiple teams including business, technology, operations, and data analytics functions.
Strong technical expertise in both classical machine learning and advanced NLP/generative AI techniques.